Optimizing Streamlit for Production ML/DL Gen AI Projects: Enhancing Performance with RQ | by Kshitij Kutumbe | May, 2024

Streamlit has gained recognition for its ease of use in creating ML/DL Gen AI venture interfaces. Nevertheless, as initiatives scale, long-running duties can influence efficiency. This weblog explores how you can mix Streamlit with RQ to dump duties to a background queue, guaranteeing a smoother consumer expertise.

Challenges with Streamlit:

• Efficiency Bottlenecks: Lengthy-running duties can result in unresponsive interfaces.
• Scalability Points: Dealing with massive datasets or a number of customers concurrently can pressure Streamlit.
• Useful resource Administration: Streamlit’s synchronous nature may be resource-intensive for advanced duties.

Introducing RQ and Background Activity Execution:

RQ (Redis Queue) is a Python library for process queueing with Redis. It allows background process execution, guaranteeing Streamlit stays responsive.

Key Ideas of RQ:

• Queue: Holds duties to be executed. RQ manages process execution order.
• Employee: Processes that execute duties. Every employee listens to particular queues.

Combining Streamlit with RQ

Folder Construction:

venture/
├── streamlit_app.py
├── rq_worker.py
└── duties.py

Setting Up Redis Server:

• Set up and run Redis server domestically or use a cloud-based service.
• Notice the Redis server’s host and port for configuration.

Configuring RQ:

# rq_worker.py
import os
from rq import Employee, Queue, Connection
hear = ['high', 'default', 'low']
redis_url = os.getenv('REDIS_URL', 'redis://localhost:6379')
conn = Connection(redis_url)
if __name__ == '__main__':
with Connection(conn):
employee = Employee(map(Queue, hear))
employee.work()

Offloading Lengthy-Operating Duties:

# streamlit_app.py
import streamlit as st
from rq import Queue
from redis import Redis
from duties import long_running_task
# Hook up with Redis server
redis_conn = Redis()
queue = Queue('default', connection=redis_conn)
def run_task():
job = queue.enqueue(long_running_task)
st.write('Activity enqueued!')
st.write('Activity ID:', job.id)
if st.button('Begin Lengthy Activity'):
run_task()

Writing Activity Operate:

# duties.py
def long_running_task():
# Your long-running process right here
move

Operating the Software:

• Run the RQ employee for the ‘default’ queue utilizing rq employee command.
• Run the Streamlit utility utilizing streamlit run streamlit_app.py command.

Conclusion

Combining Streamlit with RQ can considerably enhance the efficiency and scalability of your ML/DL Gen AI initiatives. By offloading long-running duties to a background queue, you’ll be able to guarantee a smoother consumer expertise and environment friendly process processing. This integration permits Streamlit to concentrate on offering a responsive consumer interface whereas RQ handles the heavy lifting within the background.